U.S. patent application number 14/852523 was filed with the patent office on 2016-01-07 for bicycle differentiation using video data analytics.
The applicant listed for this patent is ITERIS, INC.. Invention is credited to YAN GAO, ROBERT J. HWANG, TODD W. KRETER, WING LAM, MATTHEW LINTON, MICHAEL T. WHITING.
Application Number | 20160005312 14/852523 |
Document ID | / |
Family ID | 55017394 |
Filed Date | 2016-01-07 |
United States Patent
Application |
20160005312 |
Kind Code |
A1 |
GAO; YAN ; et al. |
January 7, 2016 |
BICYCLE DIFFERENTIATION USING VIDEO DATA ANALYTICS
Abstract
A vehicular observation and detection apparatus and system
incorporates a detection framework using video analysis to
differentiate between motorized vehicles and bicycles for improved
traffic flow and safety at intersections. The detection framework
creates virtual zones overlaid on lanes of a roadway and analyzes
input data representing objects in the virtual zones collected from
one or more cameras positioned at or near the roadway.
Inventors: |
GAO; YAN; (PLACENTIA,
CA) ; HWANG; ROBERT J.; (BREA, CA) ; LAM;
WING; (ANAHEIM, CA) ; KRETER; TODD W.;
(IRVINE, CA) ; WHITING; MICHAEL T.; (RANCHO SANTA
MARGARITA, CA) ; LINTON; MATTHEW; (TUSTIN,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ITERIS, INC. |
SANTA ANA |
CA |
US |
|
|
Family ID: |
55017394 |
Appl. No.: |
14/852523 |
Filed: |
September 12, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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PCT/US2014/022874 |
Mar 10, 2014 |
|
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14852523 |
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Current U.S.
Class: |
340/937 |
Current CPC
Class: |
G08G 1/04 20130101; G08G
1/087 20130101; G08G 1/015 20130101; G06K 9/00785 20130101; G08G
1/08 20130101 |
International
Class: |
G08G 1/015 20060101
G08G001/015; G06T 7/20 20060101 G06T007/20; H04N 7/18 20060101
H04N007/18; G06K 9/00 20060101 G06K009/00 |
Claims
1. A method of detecting a presence of bicycles in a roadway for
traffic signal controller management, comprising: creating a
virtual bicycle zone in a field of view that includes in one or
more lanes of a roadway near a traffic intersection; generating a
bicycle detection strip in or adjacent to the virtual bicycle zone;
detecting motion inside the bicycle detection strip using an object
detection means; and initiating one or more data processing
functions embodied in a plurality of instructions configured to
classify a type of object present as the object travels through the
bicycle detection strip, the one or more data processing functions
including determining whether the object present in the bicycle
detection strip is classified as either a bicycle or as a motorized
vehicle by analyzing a plurality of pixels that experience changes
from frame to frame in a video data stream as the object passes
through.
2. The method of claim 1, further comprising expanding the virtual
bicycle zone to account for a camera angle relative to the field of
view.
3. The method of claim 1, wherein the bicycle detection strip has
an independent size and shape relative to the virtual bicycle
zone.
4. The method of claim 1, wherein the bicycle detection strip is
automatically generated every time a virtual bicycle zone is
created.
5. The method of claim 1, wherein a size and location of the
bicycle detection strip varies depending on a location of the
virtual bicycle zone in the field of view.
6. The method of claim 1, wherein the object detection means is at
least one video camera positioned above or near the roadway.
7. The method of claim 1, wherein the additional data from the
object detection means differentiating bicycles from motorized
vehicles is a speed of the object.
8. The method of claim 1, further comprising creating a virtual
vehicle zone for detecting a presence of motorized vehicles in the
one or more lanes of the roadway.
9. A vehicular detection apparatus comprising: a virtual bicycle
zone overlaid on a field of view that includes on one or more lanes
of a roadway near a traffic intersection; a bicycle detection strip
applied to the virtual bicycle zone; one or more detection devices
arranged relative to the one or more lanes of the roadway surface
to sense a presence of an object in the bicycle detection strip and
generate one or more signals for determination of whether the
object is a bicycle or a motorized vehicle; a plurality of modules
configured to apply data processing functions to the one or more
signals generated by the plurality of detection devices, the one or
more data processing functions embodied in a plurality of
instructions configured to classify a type of the object present as
the object travels through the bicycle detection strip, the data
processing functions configured to determine whether the object
present in the bicycle detection strip is classified as either a
bicycle or as a motorized vehicle by analyzing a plurality of
pixels that experience changes from frame to frame in a video data
stream as the object passes through.
10. The apparatus of claim 9, wherein the plurality of detection
devices include at least one video camera positioned at an angle
relative to the field of view.
11. The apparatus of claim 9, wherein the virtual bicycle zone and
the bicycle detection strip are generated by a detection processor
located proximate to an intersection traffic controller responsible
for traffic signal management at the intersection.
12. The apparatus of claim 9, further comprising an expansion of
the virtual bicycle zone to account for a camera angle relative to
the field of view.
13. The apparatus of claim 9, wherein the bicycle detection strip
has a unique height and a unique width relative to the virtual
bicycle zone.
14. The apparatus of claim 9, wherein the bicycle detection strip
is automatically generated every time a virtual bicycle zone is
created.
15. The apparatus of claim 9, further comprising a virtual vehicle
zone for detecting a presence of motorized vehicles, overlaid on
the field of view.
16. The apparatus of claim 16, wherein the virtual bicycle zone and
the virtual vehicle zone overlap in the field of view.
17. A method of performing traffic management in an intended area,
comprising: collecting input data triggered by movement of an
object into a bicycle detection strip within or adjacent to a
virtually-created bicycle zone in a field of view that includes one
or more lanes of a roadway proximate to a traffic intersection, the
input data collected from a plurality of detection devices located
in or near each lane of the roadway; performing one or more data
processing functions embodied in a plurality of instructions
configured to classify a type of object from the input data as the
object travels through the bicycle detection strip, the one or more
data processing functions including determining whether the object
present in the bicycle detection strip is classified as either a
bicycle or as a motorized vehicle by analyzing a plurality of
pixels that experience changes from frame to frame in a video data
stream as the object passes through; and generating output data in
one more signals to a traffic signal controller to adjust a timing
of at least one traffic light in the traffic intersection where a
bicycle is detected.
18. The method of claim 17, further comprising generating the
bicycle detection strip in or adjacent to the virtually-created
bicycle zone.
19. The method of claim 18, further comprising automatically
generating the bicycle detections strip each time the
virtually-created bicycle zone is created, wherein the bicycle
detection strip has an independent size and shape relative to the
virtually-created bicycle zone.
20. The method of claim 17, further comprising expanding the
virtually-created bicycle zone to account for a camera angle
relative to the field of view.
21. The method of claim 17, further comprising detecting the object
in the bicycle detection strip using the plurality of detection
devices, the plurality of detection devices including at least one
video camera.
22. The method of claim 17, further comprising generating a
virtually-created vehicle zone for detecting a presence of
motorized vehicles.
23. The method of claim 22, wherein the virtually-created bicycle
zone and the virtually-created vehicle zone overlap in the field of
view.
24. A method of detecting a presence of bicycles in a roadway for
traffic signal controller management, comprising: creating a
virtual bicycle zone in a field of view that includes in one or
more lanes of a roadway near a traffic intersection; generating a
bicycle detection strip in the virtual bicycle zone; detecting
motion inside the bicycle detection strip using an object detection
means; and initiating one or more data processing functions
embodied in a plurality of instructions configured to classify a
type of object present as the object travels through the bicycle
detection strip, the one or more data processing functions
including determining whether the object present in the bicycle
detection strip is classified as either a bicycle or as a motorized
vehicle by analyzing a plurality of pixels in a video data stream
as the object passes through by a comparison with other identified
characteristics or features.
25. The method of claim 24, further comprising expanding the
virtual bicycle zone to account for a camera angle relative to the
field of view.
26. The method of claim 24, wherein the bicycle detection strip has
an independent size and shape relative to the virtual bicycle
zone.
27. The method of claim 24, wherein the bicycle detection strip is
automatically generated every time a virtual bicycle zone is
created.
28. The method of claim 24, wherein a size and location of the
bicycle detection strip varies depending on a location of the
virtual bicycle zone in the field of view.
29. The method of claim 24, wherein the object detection means is
at least one video camera positioned above or near the roadway.
30. The method of claim 24, wherein the additional data from the
object detection means differentiating bicycles from motorized
vehicles is a speed of the object.
31. The method of claim 24, further comprising creating a virtual
vehicle zone for detecting a presence of motorized vehicles in the
one or more lanes of the roadway.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATIONS
[0001] This patent application is a continuation of, and claims
priority to, Patent Cooperation Treaty application no.
PCT/US2014/022874, with an international filing date of 10 Mar.
2014, and a Convention priority date of 13 Mar. 2013, pursuant to
35 U.S.C. .sctn.363, .sctn.365(c) and .sctn.120, and pursuant to 37
C.F.R. .sctn.1.53(b). The contents of the above-referenced Patent
Cooperation Treaty application are incorporated in their entirety
herein. In accordance with 37 C.F.R. .sctn.1.76, a claim of
priority as a continuation application to the above-referenced
Patent Cooperation Treaty application is included in an Application
Data Sheet filed concurrently herewith.
FIELD OF THE INVENTION
[0002] The present invention relates generally to traffic
observation and detection. More specifically, particular
embodiments of the invention relate to traffic control systems, and
to methods of observing and detecting the presence and movement of
bicycles in traffic environments using data derived from cameras
collecting video data and other sensors.
BACKGROUND OF THE INVENTION
[0003] There are many conventional traffic detection systems.
Conventional detectors typically utilize sensors, either in the
roadway itself, or positioned at a roadside location or on traffic
lights. The most common type of vehicular sensors are inductive
coils, or loops, embedded in a road surface. Other existing systems
utilize video cameras, radar sensors, acoustic sensors, or
magnetometers, either in the road itself, or at either the side of
a roadway or positioned higher above traffic to observe and detect
vehicles in a desired area.
[0004] While much attention has been paid to detecting motorized
vehicles such as cars, trucks and motorcycles as they move through
intersections, lesser attention has been given to the safe movement
of bicycles. Bicycles are an important component of the traffic
environment in many places, and create unique challenges to
integrate them safely into the movement of vehicles throughout
roadways, especially in urban areas.
[0005] Traditionally, bicycles have been detected via a variety of
methods, such as special loops, video cameras, thermal cameras,
micro radar in ground systems, and even manual push buttons. Yet
each of these can be inaccurate means of robust and reliable
detection, which may lead to comprised safety for bicyclists as
they navigate traffic thoroughfares. The need for accurate bicycle
detection generally involves ensuring that bicyclists have priority
due to the greater possibility of injury from, and accidents with,
higher-speed and larger motorized vehicles. For example, when a
bicycle arrives at an intersection, the rider wishes to have the
signal actuated so that the rider can safely cross the intersection
under the green light in situations where the bicycle is accorded
priority. Similarly, when crossing the intersection, the rider
needs adequate time to safely cross under the green signal phase.
For most intersections, there is a minimum green time that is set
so that if only one motorized vehicle is detected, the light will
stay green for only a short amount of time. Often this time is not
adequate for a bicycle to cross the intersection safely under the
green phase, due to the fact that bicycles are slower than
motorized vehicles and therefore may need extra time for the
minimum green phase. One problem facing traffic planners and
engineers is that they typically do not set the minimum green time
to account for bicyclists, because it would be used every signal
cycle, whether a bicycle is present or not. So there is a need in
conventional systems for uniquely differentiating bicycles from
motorized vehicles so that these minimum times can be applied
appropriately.
[0006] Traffic planners and engineers also require data on the
volume of traffic at key points in a traffic network. This data is
important for comparing volumes over periods of time to help with
accurate adjustment of signal timing. Current methods of traffic
detection result in a data collection that results only from a
count of a total number of vehicles, which may or may not include
bicycles. As the need for modified signal timing to accommodate
bicyclists, as described above, becomes more critical for proper
traffic management, a method for separating the count of bicycles
from the count of other vehicles on a thoroughfare would greatly
improve the ability to accurately manage traffic environments.
BRIEF SUMMARY OF THE INVENTION
[0007] The present invention discloses a bicycle detection system
and method, and an associated method of performing traffic
management in an intended area such as a traffic intersection. The
bicycle detection system includes at least one camera, a housing,
and circuitry capable of performing processing from data generated
by the at least one camera. Methods of performing traffic
management according to the present invention utilize this data to
analyze traffic in a variety different situations and
conditions.
[0008] The present invention achieves numerous objectives
representing advancements over the existing art of conventional
traffic detection systems. For example, the present invention
provides an inclusive framework which does not require two separate
systems to detect bicycles and vehicles. The present invention also
provided greater flexibility for bicyclists to enjoy a higher
degree of freedom in terms of movement, as compared with
conventional detection systems that require the bicycles to be
detected at a particular spot.
[0009] The present invention also provides enhanced signal and
traffic safety. By properly detecting bicycles, traffic signal
environments can safely provide passage for bicycle riders, while
at the same time maintaining efficient operations when no bicycles
are present.
[0010] Other objectives, embodiments, features and advantages of
the present invention will become apparent from the following
description of the embodiments, taken together with the
accompanying drawings, which illustrate, by way of example, the
principles of the invention.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0011] The accompanying drawings, which are incorporated in and
constitute a part of this specification, illustrate several
embodiments of the invention and together with the description,
serve to explain the principles of the invention.
[0012] FIG. 1 is an example of an existing system in which virtual
detection zones are identified;
[0013] FIG. 2 is a further example of an existing system in which
virtual detection zones are identified;
[0014] FIG. 3 is a diagram of a motorized vehicle and a bicycle in
a roadway with detection zones in a differentiated detection
framework according to the present invention;
[0015] FIG. 4 is a further diagram of a differentiated detection
framework according to the present invention;
[0016] FIG. 5 is a diagram of an expanded bicycle zone to account
for camera angle according to the present invention;
[0017] FIG. 6 is a diagram of a motorized vehicle and a bicycle in
a roadway with detection zones in a differentiated detection
framework according to one embodiment of the present invention;
and
[0018] FIG. 7 is a diagram of a motorized vehicle and a bicycle in
a roadway with detection zones in a differentiated detection
framework according to another embodiment of the present
invention.
DETAILED DESCRIPTION OF THE INVENTION
[0019] In the following description of the present invention
reference is made to the accompanying figures which form a part
thereof, and in which is shown, by way of illustration, exemplary
embodiments illustrating the principles of the present invention
and how it is practiced. Other embodiments will be utilized to
practice the present invention and structural and functional
changes will be made thereto without departing from the scope of
the present invention.
[0020] Historically, virtual detection zones used to identify the
presence of vehicles in traffic detection systems are capable of
detecting both motorized vehicles and bicycles, but are unable to
accurately differentiate between them. FIG. 1 and FIG. 2 are an
example of an existing system in which virtual detection zones are
identified. FIG. 1 shows a typical thoroughfare arrangement where
there are multiple vehicle zones, usually corresponding to marked
lanes on a roadway. Roadways often have larger marked lanes for
motorized vehicles and a smaller, bicycle-specific lane that is
marked as such.
[0021] Virtual traffic detection zones typically employ multiple
sensors, such as in-ground "loop" sensors, radar sensors, and video
cameras, to try and detect the presence of vehicles. However,
neither of these is able to adequately determine the difference
between a motorized vehicle, such as a car, and a bicycle.
In-ground sensors, such as inductive loops, are unable to detect
bicycles that travel into vehicular lanes. Video cameras are able
to detect a bicycle's presence, but it will be detected as a
vehicle, with no knowledge of what type of vehicle it is. Radar
sensors are able to detect a vehicle's presence approaching or at
an intersection, but also are unable to accurately determine what
type of vehicle it is. FIG. 2 shows illustrates this fundamental
problem--bicycles often travel into vehicular lanes, and are often
mis-detected as vehicles.
[0022] The present invention provides a detection system and method
that is capable of differentiating between bicycles and motorized
vehicles such as cars to uniquely provide accurate information to
the traffic signal controller for more efficient and safe operation
of a traffic environment. The detection system and method is
intended to be integrated into a vehicular observation and
detection apparatus incorporating sensors that include one or more
of video cameras, radar, and in-pavement inductive loops. The
vehicular observation and detection apparatus is to be mounted on
or near a traffic signal, at a position above a roadway's surface,
to enable optimum angles and views for detecting vehicles in the
one or more intended areas with both the radar sensor and the
camera.
[0023] FIGS. 3-7 demonstrate a framework and embodiments thereof
for detecting differences between motorized vehicles and bicycles
and confirming the accuracy of the initial observation. FIG. 3 and
FIG. 4 are diagrams of traffic environments 102 illustrating a
bicycle detection framework 100 according to the present invention,
showing a bicycle 110 in a bicycle-specific lane 120, and a
motorized vehicle 140 in a regular lane 150. The present invention
creates unique bicycle zones 130 and virtually applies them to
lanes in traffic thoroughfares 102 (which may also herein be
referred to as roadways 102 or traffic environments 102) so that
detections only occur when a bicycle 110 goes through the bicycle
zone 130. Using the detection framework 100 described herein, if a
vehicle 140 goes through the unique bicycle zone 130, detection is
not activated. These bicycle-specific detection zones 130 can be
applied anywhere in the traffic environment 102, including in
vehicular traffic lanes 150, to detect only bicycles 110. This
means that bicycles 110 traveling in normal vehicular traffic lanes
150 will be correctly identified as bicycles 110 so that
appropriate signaling of the traffic controller can be triggered.
Similarly, this means that motorized vehicles 140 that have entered
the bicycle lane 120 will also be correctly identified as motorized
vehicles 140, and not bicycles 110. This provides a truly flexible
system for the traffic engineer to put in place, and provides the
ability to count bicycles 110 separate from other vehicles 140.
[0024] In the detection framework 100 of the present invention, for
each area of a roadway 102 in which a traffic engineer or other
user would like to detect the presence of bicycles 110, a virtual
bicycle zone 130 is created and positioned in vehicular lanes 150
and/or bicycle-specific lanes 120 as desired. Additionally, a
bicycle detection strip 132 is automatically generated on top of
and at a specified point adjacent to each virtual bicycle zone
130.
[0025] The bicycle detection strip 132 is an extension of the
virtual bicycle zone 130 and is generated based on a number of
considerations. It is an area adjacent to the virtual bicycle zone
130 that is automatically generated and represents an initial
"triggering" area of the virtual bicycle zone 130 that is invisible
to the traffic engineers and other users. The bicycle detection
strip 132 has the same orientation as the bicycle zone 130, but has
its own height and width.
[0026] The size of the bicycle detection strip 132 varies depending
on the location of the virtual bicycle zone 130, its size, and the
number and location of surrounding zones. When motion inside the
bicycle detection strip 132 is detected, the present invention
proceeds with determining what type of object is present within the
virtual bicycle zone 130 as the object moves through the detection
strip 132. The bicycle detection strip 132 therefore operates as a
triggering area as noted above, so that when motion is observed
using at least one of means of detection (for example, one or more
video camera) available, additional data processing functions are
initiated and applied to the rest of the virtual bicycle zone 130
to make an accurate determination of the type of object
present.
[0027] In one aspect, the present invention uses object pattern
recognition in an attempt to determine if the object present in the
bicycle detection strip 132 is a narrow object 112 or a wide object
142. If it is a narrow object 112, and detection is indicated in
the virtual bicycle zone 130, then a bicycle 110 will be
determined. If it is a wide object 142 and detection is indicated
in the virtual bicycle zone 130, a regular motorized vehicle 140
will be determined, and the detection will be rejected as "not a
bicycle."
[0028] The detection framework 100 of the present invention
therefore attempts to classify all moving objects into two classes:
narrow moving objects 112 (assumed to be bicycles 110) and wide
moving objects 142 (assumed to be motorized vehicles 140). In the
bicycle detection strip 132, the present invention analyzes pixels
that experience changes from frame to frame in a video data stream
taken by one or more video cameras. Pixels within this stream are
analyzed as the object passes through the bicycle detection strip
132 on a frame-by-frame basis. From this pixel-based analysis, the
present invention derives an initial decision of whether the object
moving through the bicycle detection strip 132 is wide 142 or
narrow 112.
[0029] Therefore, object pattern classification is used in the area
defined by the bicycle detection strip 132. The detection framework
100 does not rely solely on this decision, however. While the
bicycle detection strip 132 provides a good initial decision of
whether the object which is coming to the virtual bicycle zone 130
is wide 142 or narrow 112, the present invention performs further
data processing to confirm or reject the decision from the
pixel-based analysis. The detection framework 100 further analyzes
characteristics of objects inside the virtual bicycle zone 130 to
confirm or reject the initial decision as more information becomes
available as the object passes through the virtual bicycle zone
130. For example, one characteristic is expected gray scale changes
in images from the virtual bicycle zone 130. Once an object arrives
in the bicycle zone 130 as indicated by its motion in the bicycle
detection strip 132, the present invention looks for gray scale
changes in images taken over time as the object passes through the
bicycle zone 130. Another characteristic is predicted motion. The
detection framework 100 attempts to compare behavior of an object
with expected behavior of both a bicycle 110 and a motorized
vehicle 140. Furthermore, a series of detection rules may also be
applied to confirm or reject the initial decision from the bicycle
detection strip 132. Examples of detection rules involve a speed of
the object, visibility of a person riding on the bicycle 110 or in
a motorized vehicle 140, visibility of vehicle registration tags,
whether and how quickly an object begins to charge an inductive
loop embedded in the roadway 102, and other characteristics
differentiating bicycles 110 from motorized vehicles 140. One or
more outcomes of these characteristic analyses are then applied to
confirm or reject an earlier decision based on the bicycle
detection strip 132 for the presence of a bicycle 110 in the
virtual bicycle zone 130.
[0030] It should be noted that means of detection as contemplated
by the present invention may include other detection devices. For
example radar sensors may be incorporated into the detection
framework 100, as well as inductive loops embedded in a roadway
102. In the present invention, multiple means of detection may be
utilized to collected input data for performing the one or more
data processing functions disclosed herein. It is therefore
contemplated that data from multiple detection sensors may be
incorporated and are within the scope of the present invention.
[0031] FIG. 5 shows an aspect of the present invention in which an
expanded virtual zone 134 is created to extend a virtual bicycle
zone 130 to account for the angle of a video camera above or next
to a roadway 102, intended to capture a bicyclist and correctly
detect the presence of the bicycle 110. This situation is desirable
where a camera is not mounted directly above the bicycle lane 120,
and the virtually-created bicycle zone 130 with a rectangular
orientation may not suffice to detect a bicycle rider's body.
Because of the angled position of the camera relative to the
bicycle lane 120, the body of the bicycle rider may appear out of
the regularly-oriented zone, and analysis of images in the bicycle
zone 130 may mis-detect the object and fail to properly adjust
signaling to account for the presence of a bicyclist. Therefore,
the virtual bicycle zone 130 is expanded to form a parallelogram
shape. This attribute therefore extends the zone in an attempt to
capture the rider.
[0032] FIG. 6 shows one embodiment of the present invention in
which virtual zones for detection may be overlaid on each other, so
that when a motorized vehicle 140 such as a car is detected as
being at a "stop" bar 170, it is correctly detected as a motorized
vehicle 140 and a vehicular detection zone 160 is activated, but
the expanded virtual bicycle zone 134 is not activated. When a
bicycle 110 is present and at the stop bar 170, it is initially
detected as a bicycle 110, but both the vehicular detection zone
160 and the bicycle zone 130 are activated and overlap on each
other.
[0033] In this aspect of the present invention, logic is then
applied to determine the proper detection. If the video (or, where
applicable, radar, and/or loop sensors) indicate the presence of a
vehicle 140 in overlapping virtual detection zones 130 and 160, car
detection is activated to confirm the presence of a motorized
vehicle 140. If the video, radar, and or/or loop sensors indicate
the presence of a bicycle 110 in overlapping virtual detection
zones 130 and 160, the extended virtual bicycle zone 134 is
activated to confirm the presence of a bicycle 110.
[0034] FIG. 7 illustrates a detection framework 100 according to
another embodiment of the present invention, in which a
commonly-oriented detection zone 180 is established within which
both bicycles 110 and motorized vehicles 140 are uniquely detected.
This detection zone 180 provides two separate outputs--one for a
motorized vehicle 140 detection and a second for a bicycle 110
detection. In this embodiment, one virtual zone 180 is drawn for
each lane in a roadway 102, and detection analyses discussed herein
are performed within each zone 180 for a determination of the type
of object present. In this embodiment, instead of making an initial
determination of whether the object is a motorized vehicle 140 or a
bicycle 110 based on the application of specific virtual bicycle
zones 130 (whether it includes a bicycle detection strip 132 or is
overlaid on a vehicle zone 160), each detection zone 180 is
virtually created in the same manner, and is capable of generating
two outputs: one where the object present is a motorized vehicle
140, and one where the object present is a bicycle 110. In other
words, instead of attempting to initially predict the type of
vehicle from application of a zone specific to a type of vehicle,
in this embodiment, the present invention creates the same virtual
zone 180 for each, and performs the ancillary processing discussed
herein to make a determination about what type of object is
present. If this processing determines a motorized vehicle 140, one
set of output signals is generated. Similarly, if this processing
determines a bicycle 110 is present, another set of output signals
is generated.
[0035] Regardless of the embodiment, the present invention is
intended to provide output data that performs traffic signal
control by adjusting traffic lights to accommodate the presence of
bicyclists. The detection framework 100 disclosed herein is
communicatively connected with a traffic signal controller
proximate to a traffic intersection for which thoroughfares 102 are
analyzed, and generates signals as output data to instruct the
traffic controller based on the data analytics performed. As noted
above, for most intersections, there is a minimum green time that
is set so that if only one vehicle is detected, and if so, the
traffic light will stay green for only a short amount of time.
Often this time is not adequate for a bicycle 110 to cross the
intersection safely under the green phase, due to the fact that
bicycles 110 are slower than motorized vehicles 140 and therefore
may need extra time for the minimum green phase. The present
invention therefore improves bicycle safety and provides a
mechanism for conferring signal priority for bicyclists.
[0036] It is to be understood that other embodiments will be
utilized and structural and functional changes will be made without
departing from the scope of the present invention. The foregoing
descriptions of embodiments of the present invention have been
presented for the purposes of illustration and description. It is
not intended to be exhaustive or to limit the invention to the
precise forms disclosed. Accordingly, many modifications and
variations are possible in light of the above teachings. For
example, the present invention may overlay a third type of
detection zone that is intended to account for the presence of mass
transit vehicles. Similarly, in addition to making an initial
determination of whether an object is wide or narrow, the present
invention may include a third or fourth size criteria for objects,
such as for example "long" or "high" to predict the presence of
mass transit vehicles. It is therefore intended that the scope of
the invention be limited not by this detailed description.
* * * * *